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Rolling bear is a major critical component of rotating machinery, as its working condition affects the performance of the equipment. As a result, the condition monitoring and fault diagnosis of bearings get more and more attention. However, the strong background noise makes it difficult to extract the bearing fault features exactly. Furthermore, regular gradient disappearance and overfit appear in traditional network model training. Therefore, taking the printing press bearings as the research object, an intelligent fault diagnosis method based on strong background noise is proposed. This method integrates frequency slice wavelet transform (FSWT), deformable convolution and residual neural network together, and realizes the high-precision fault diagnosis of the printing press bearings. First, FSWT is used to preprocess the original vibration signal to obtain bearing fault features in the time and frequency domain, reconstruct the signal in any frequency band and describe local features accurately. Second, the ResNet is selected as the base network, and the two-dimensional time-frequency diagrams (TFD) obtained by preprocessing are used as input. For the model that has a poor ability to extract subtle features under strong background noise, the deformable convolution layer is introduced to reconstruct the convolution layer of ResNet, called deformable convolution residual neural network (DC-ResNet). Finally, the effectiveness of this method is verified by using the data sets collected under experimental conditions and actual working conditions for fault diagnosis of the printing press. The results show that the DC-ResNet can classify different bearing faults under strong background noise, and the accuracy and stability are greatly improved, which the accuracy meets 93.90%. The intelligent fault diagnosis with high-precision of printing press bearings under complex working conditions is realized by the proposed method. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the help of deep learning methods. However, traditional intelligent fault diagnosis methods still have some limitations in fault feature extraction and the latest object detection theory has not been applied in fault diagnosis. To this end, a fault diagnosis method based on a sparse short-term Fourier transform (SSTFT) and object detection theory is developed in this paper. First, a sparse constraint is introduced in time-frequency analysis to improve the time-frequency resolution of the model without cross-term interference and proximal gradient descent (PGD) is adopted to quickly and effectively optimize the model to obtain a high-quality time-frequency representation (TFR). Second, a fault diagnosis model based on a region-based convolutional neural network (RCNN) is built; the model can extract multiple regions that can characterize fault features from the TFR. This process avoids the interference of irrelevant vibration components and improves the interpretability of the fault diagnosis model. Finally, multicategory rolling bearing fault identification is realized. The effectiveness of the proposed method is validated by simulation signals and bearing experiments. The results indicate that the proposed method is more effective than existing methods. [ABSTRACT FROM AUTHOR]

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods. [ABSTRACT FROM AUTHOR]

• A fault signal detection method based on object detection is proposed. • Using the fault feature regions (FFRs) to detect the bearing fault impact features. • Realizing the compound fault diagnosis of bearing with single fault data. • Showing the superior performance of accuracy and efficiency of fault diagnosis. Bearing faults of rotating machinery are common compound faults, and diverse fault categories are coupled, which makes it challenging to achieve state monitoring. For this purpose, a fault diagnosis method based on fault feature region (FFR) detection for bearings is proposed. The key point is an FFR proposal network for several regions with fault features from training samples. This process only uses the single fault data. These obtained regions are used as the training dataset of a module based on a deep belief network. In the application, if the output probabilities satisfy the discrimination terms, then an untrained compound fault state is output. Furthermore, the bearing compound fault dataset is employed to assess the diagnosis performance. The results reveal that the diagnosis accuracy of compound faults and the overall accuracy exceeds 80%. This fascinating discovery proves the superiority of the proposed approach to achieving compound fault diagnosis for rolling bearings. [ABSTRACT FROM AUTHOR]